How to avoid one-off efforts and instead implement across the enterprise

These days, manufacturers are looking to jumpstart operational productivity, efficiency, and profitability via cutting-edge digital technologies like IIoT, AI, advanced analytics, and machine learning. With billions in near-term gains and cost savings projected across industries, this new wave of innovation has been dubbed Industry 4.0 — no less than a fourth industrial revolution.

The potential is very real… but there’s a lot of ground to cover to achieve it. Just a few short years ago, digital initiatives were mostly marked by false starts and major expenditures on one-off pilots that, disappointingly, didn’t scale. More recently, hard-won lessons and newer solutions are fueling some successes. Still, the vast majority of manufacturers are having a hard time pivoting away from “pilot purgatory.” We still see too many companies flailing about in too many directions, with internal teams independently targeting tactical initiatives that don’t span across the enterprise and translate to bottom-line value. Why do so many companies get so stuck? Well, most seem to fall victim to the same set of mistakes.

Siloed approaches going every which way

Often, the fundamental problem is a lack of cohesive, unified vision: projects undertaken without a long-term strategy, executive ownership, or explicit endorsement from senior management. In the resulting vacuum, IT and OT continue to chase innovation as they’ve done historically: on separate tracks, without much thought to what their counterparts are doing. As for the new data-focused “hit squads” within IT — composed of data engineers, data architects, and UX specialists — they have yet to be embraced by either of the traditional groups.

Happily, our experience of working with manufacturers has yielded a few examples of effective digital innovation. Here are guiding principles distilled from these notable successes.

Start with Strategy

Drive the digital vision from the top down. As mentioned earlier, digital projects typically originate autonomously at the team level. IT, for example, may come up with an intriguing idea and execute on its own, only to discover at rollout that the solution doesn’t integrate with production processes and systems, or isn’t aimed at the gains OT values and therefore has little benefit for the organization as a whole.

The answer is to start at the top by building conviction for digital innovation with senior management: decision-makers and influencers with a broad understanding of the resources required and the power to marshal them across the enterprise. Success at scale takes careful management of technologies, use cases, investments, and cultural ramifications. Work together with management to develop an ROI roadmap geared to the size and nature of the objective, and allocate IT and OT resources accordingly.

Avoid “shiny object syndrome.”Rather than getting seduced by exciting-looking technology, begin with a clear-headed analysis of how a solution can address your particular pain points, build competitive advantage, and drive bottom-line impact. For instance, 3D printing is all the rage right now. It should be, in sectors where it enables designs that would otherwise be impossible to manufacture, or provides on-the-spot support for operational needs (such as spare parts). In other industries, though, it’s not much more than an expensive distraction. Apply the same logic to help focus your own efforts. For example, if you’re asset-heavy, prioritize predictive maintenance. If your primary cost is labor, concentrate on digital performance management.

Take the long view.More than a few vendors are pitching spot solutions that provide only limited value. Look past immediate fixes and go for a sustainable approach that builds long-term competitive advantage.

Design Your Technology Stack

IT deficiencies can sink digital strategies if not caught and corrected early on. So carefully plan your architecture up front. Too many companies don’t and end up with massively over- or under-engineered technology stacks. On one extreme we find single bloated solutions deployed on the mistaken belief that they “can do everything.” On the other, we see 30 different platforms. Either scenario puts you in a hole that’s hard to dig out of.

Ask the right questions in the right order.As suggested earlier, start with a vision of where you want to go. What are you trying to improve? How can digital do it? Make sure that IT’s goals are aligned with what OT seeks to achieve. Secondly, identify technology tools and infrastructure that enable the benefits, and work up use cases for them. Here too, the importance of cross-functional collaboration cannot be exaggerated: if OT co-ownership is not solicited early on, the project is unlikely to achieve strategic impact down the road.

Mobilize all your data. Capturing and harmonizing your universe of data is foundational to digital innovation.It takes an engine that unifies both IT and production data — especially the streaming kind — to unlock the transformative power of technologies like AI and machine learning. Design and develop your IT architecture to enable this integration.

Don’t try to build everything yourself. Technology is changing rapidly, and attempting to keep up on all fronts internally is a tough task. Choose partners that provide the underlying capabilities you don’t have and can’t develop quickly in-house. Look for solutions and providers that accommodate open integration standards and allow you to tailor the system to your business needs.

Build the Skills and Bench Strengths You Need

Everyone wants digital innovation to “disrupt” — that is, to dramatically enhance profitability and competitive advantage. Frequently overlooked are the internal disruptions that new deployments also bring: major changes to accustomed processes, workflows, job requirements, and company culture. These upheavals and transitions must be diligently managed. Otherwise, digital initiatives can languish in the “science project” mode. Translate your objectives into concrete business processes and train employees to apply them. It’s not feasible to just drop a technical solution in and expect people to learn how to use it on their own.

Empower a dedicated team to lead the charge. It takes a village — a company-wide effort — to drive and manage change, led by specialists fluent in both operations and technology, who can serve as “translators” to bridge traditional departmental silos. You’ll also need technical experts who understand the implementation, along with change management coaches and project managers with a PMO-style ownership mindset.

Nurture cross-functional collaboration. OT understands production assets and processes. IT lives in a world of data, networking, and connectivity. Building collaboration and synergy between the two disciplines is a critical and early assignment for project owners.

Fill your capability gaps. It’s rare for a manufacturer to possess all the digital transformation knowhow they need in-house. Focus on talent. Blend internal training with external recruiting and collaborate with solution providers and specialists. Selectively choose targeted partners with deep expertise and involve them in development as early as possible.

To keep things in perspective, bear in mind that new ideas and technologies can take a little while to reach the business and consumer mainstream. E-commerce, social media, and the Internet itself started as fringe novelties but soon went on to change the world. With today’s accelerated pace of change, astute value-focused decision making, and leveraging of new technologies and solutions, digital manufacturing will deliver the same magnitude of impact within an even shorter time frame.

Kevin Goering is a Partner in McKinsey’s San Francisco office. Kevin is an operations dedicated practitioner that has led numerous transformations in supply chain, procurement, manufacturing and service operations. In addition to deep operations expertise, Kevin is a leader in the North American Digital Operations practice, driving transformations to new levels of performance leveraging new data and technology capabilities.

Daya Vivek is Director, Platform Engineering at Sight Machine. She has a a 20 year track record in delivering enterprise software products and engaging customers to meet diverse market and user needs. Previously, Daya had 17 year career at IBM spanning multiple roles: an Engineering Manager for the Watson Discovery Service hosted on the IBM cloud, a Solution Architect for the Customer Enablement team for IBM’s Big Data Solution (Infosphere Biginsights), and a software developer in pureQuery (a data access platform for database clients) and IBM’s database engine DB2. Daya has a Master’s in Computer Science from Arizona State University and an MBA from Santa Clara University.

Josh Brown

DevOps Engineering Manager

Josh Brown is an Engineering Manager focusing on infrastructure and tooling for Sight Machine. For the last 15 years, Josh has worked to help scale multiple startups across many different industries, from fashion to mobile messaging. Josh uses his experience from previous startups to solve nuanced problems that span well beyond the implementation of technology.

In his personal life, Josh has also been involved in multiple humanitarian efforts, in Mexico and Haiti, and enjoys exploring the world with his family.

Ed Jimenez

VP of Marketing

Ed Jimenez is VP of Marketing for Sight Machine. Previously, Mr. Jimenez led Cisco’s Enterprise and Industry Marketing teams. He also worked as a senior consultant helping Cisco’s largest customers understand how disruptive technologies affect the customer experience. Prior to joining Cisco, Mr. Jimenez led Gartner’s Retail & Consumer Products Practice. He also spent a number of years in the retail and manufacturing industries with positions in technology transformation and operations.

Mr. Jimenez has published a number of papers on retail & manufacturing technology trends and was a regular host for the NBC Morning News Technology Report.

Mr. Jimenez earned his M.B.A. from the University of Illinois.

Harry Wornick

Director of Product

Harry Wornick is the Director of Product for Sight Machine. For the past several years, Mr. Wornick has led Sight Machine’s product efforts, from infrastructure and data pipeline, to visualization and analytics. Previously, Mr. Wornick was the Senior Product Manager at Support.com, leading the development of cloud-based customer support software.

Mr. Wornick earned his B.S. in Engineering from Harvey Mudd College, where he spent several years working with national laboratories on renewable energy research.

Ajay Nayak

Product Engineering Manager

Ajay Nayak is the Product Engineering Manager for Sight Machine. Previously, he was VP of Engineering for Bakround, a startup focused on improving the recruiting process for hiring managers and candidates using machine learning. Prior to that, he led an engineering team for Insightly, an SMB-focused CRM. He also has consulting experience at Booz Allen Hamilton and Slalom, which has enabled him to gain expertise in process improvement for a variety of industries.

Ajay has a BS in Electrical & Computer Engineering from Rutgers University, and an MEng in Systems Engineering from Stevens. He’s passionate about using technology to measurably improve societal outcomes and is actively involved in youth-oriented volunteering for his local community.

Kurt DeMaagd, PhD

Chief AI Officer & Co-Founder

Kurt co-founded Slashdot.org and has served as a professor at Michigan State University in information management, economics, and policy. Kurt is an accomplished analytics programmer.

Chris Dobbrow

SVP, Sales

Chris has over 25 years of strategic enterprise sales experience creating & executing successful go to market strategies focused on enterprise customers. He has served in senior management roles at Perforce Software, SourceForge and Ziff-Davis.

Jon Sobel

CEO & Co-Founder

Jon has served on the management teams of several companies in pioneering industries, including Tesla Motors, SourceForge, and in its early years, Yahoo! Jon holds an BA from Princeton, a JD from the University of Michigan, and an MBA from Wharton.

Adam Taisch

VP of Global Sales & Co-Founder

Adam has led business development, product development and sales in innovative industries for 15 years. An early employee at Yahoo!, and a proud son of the Midwest, Adam excels at ensuring the new technologies serve client needs.

Anthony Oliver

Lead Applications Engineer & Co-Founder

Anthony has over 12 years of experience developing and deploying robotics, computer vision, and data analysis tools in the manufacturing sector. He is a multidisciplinary software engineer who is equally at home in application engineering, dev-ops, front and back end development, and vision programming.

Jerry Wu

CFO

Jerry has over 20 years of experience in technology corporate finance and public/private equity investments. Prior to his career in finance he was a manufacturing engineer with Silicon Graphics. Jerry holds BS and MS degrees in electrical engineering from Stanford and an MBA from Wharton. He is a CFA charterholder.

John Stone

VP of Business Development & Partnerships

An experienced business development executive who has worked with global brands to drive engagement, collaboration, and results across organizations at all levels.

Beth Crane

VP of Data

Beth Crane, PhD is Vice President of Data for Sight Machine, the category leader in manufacturing analytics. In this role she focuses on helping manufacturers understand how advanced analytical techniques can solve complex problems in production and operations.

Prior to Sight Machine, Beth has worked in both academia and industry and has led the development of analytical and reporting tools used for continuous process improvement.

She received her PhD and Masters of Science degrees from the University of Michigan and was awarded a National Science Foundation postdoctoral fellowship to explore the development of statistical methods for predicting dysfunction in multi-dimensional time series data.

Sudhir Arni

VP of Implementation

Sudhir Arni​ is Sight Machine’s VP of Manufacturing Transformation. Prior to joining Sight Machine, Sudhir was an engagement manager at McKinsey & Co., where he designed and led manufacturing transformation programs for pharmaceutical and chemical manufacturers. He received joint MBA and Master of Science degrees from the Kellogg School of Management and McCormick School of Engineering at Northwestern University.

Nathan Oostendorp

CTO & Co-Founder

Nathan Oostendorp is the CTO of Sight Machine, he co-founder the company in 2011. Nathan started his career as a controls engineer at Donnelly Corporation (now Magna Mirror) where he worked on PLC programming, computer vision, data acquisition, and robotics for a major automotive supplier.

In 1996 he co-founded Slashdot.org, a major tech news blog which was the center of the Linux and Open Source Software movement. During this period he spun off several other successful open online communities including Everything2.com (an early precursor to Wikipedia) and PerlMonks.org, the central hub for the Perl programming Language. He also created the first Open Source advertising and analytics platform. He then joined SourceForge.net as the site architect and ushered it through a period of growth where it became a top 100 website globally, and hosted several hundred thousand software projects.

He holds a BS in Computer Science from Hope College in Holland Michigan, and an MSI in Information Science from the University of Michigan.

10 Hot AI-powered IoT startups

The Internet of Things generates a lot of data that needs to be processed, and innovative startups recognize that artificial intelligence can lighten the load. Jeff Vance of Network World selected Sight Machine as a 10 hot AI-powered IoT startup. Read on to learn more about what Sight Machine does to address this.

Problem Sight Machine solves: Manufacturers struggle to make optimum decisions quickly. When dealing with problems that emerge on the plant floor, any indecision or delay in decision making could be costly.

In manufacturing, data variety (due to thousands of sources) is far greater than in other IoT use cases, and according to research from Morgan Stanley, the sheer quantity of data is also larger than anywhere else. Traditional analytics tools can’t cope with either the variety or volume.

How they solve it: Sight Machine software uses canonical data models and AI to ingest, integrate, and map massive amounts of heterogeneous data into operational models. The canonical data models represent any machine, line, facility, supplier, part or batch that the manufacturer specifies. Once modeled, data is then systematically and continuously analyzed.

By standardizing the manufacturing models and following a data-first approach to decision making, Sight Machine enables manufacturers to automate data ingestion in a rapid, highly repeatable manner. The standardized model allows manufacturers to create downstream applications that immediately leverage the modeled data.

Analytical techniques include advanced inferential statistics, machine learning and AI, all of which are applied to generate manufacturing-specific insights. Within its platform, Sight Machine analyzes and visualizes data, so results can be viewed via a browser on any connected device.

Why they’re a hot startup to watch: Sight Machine has the deepest pockets in this roundup, backed by $50 million in VC funding. CEO and co-founder Jon Sobel was previously with Tesla and Yahoo, while co-founder and CTO Nathan Oostendorp and co-founder and Chief Data Scientist Kurt DeMaagd previously co-founded Slashdot.org. Finally, the customers Sight Machine has accumulated are impressive, including GE, Fiat Chrysler, and Fujitsu.

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